Parametric Visualization of High Resolution Correlated Multi-spectral Features Using PCA

Abstract

An imaging mass spectrometer is an analytical instrument that can determine the spatial distribution of chemical compounds on complex surfaces. The output of the device is a multi-spectral datacube; a three-dimensional (3D) dataset in which the xy-dimension represents the surface position and the z-dimension represents the mass spectral distribution. Analysts try to discover correlations in spectral profiles and spatial distributions inside a datacube. New technological developments allow mass spectrometric imaging on a higher spatial and spectral resolution. In this paper we present a parametric visualization technique which allows an analyst to examine spectral and spatially correlated patterns on the highest possible resolution. Principal component analysis (PCA) is used to decompose the datacube into several discriminating components. We represent these extracted features as abstract geometric shapes and use three parameters to allow for data exploration. The first parameter thresholds the spectral contribution at which an extracted component is visualized. The level of detail the shapes is controlled by a second parameter and a third parameter determines at which density-level the extracted feature is represented. This new visualization technique enables an analyst to select the most relevant spectral correlations and investigate their specific spatial distribution. With this method, less noise is included in the visualization of extracted features and by introducing various levels of detail the full spectral resolution can be utilized.